import torch
from torch import nn


class LitEma(nn.Module):
    def __init__(self, model, decay=0.9999, use_num_upates=True):
        super().__init__()
        if decay < 0.0 or decay > 1.0:
            raise ValueError("Decay must be between 0 and 1")
        else:
            self.m_name2s_name = dict()
            self.register_buffer(
                "decay",
                torch.tensor(decay, dtype=torch.float32)
            )
            self.register_buffer(
                "num_updates",
                torch.tensor(0, dtype=torch.int)
                if use_num_upates
                else torch.tensor(-1, dtype=torch.int)
            )

            for name, p in model.named_parameters():
                if p.requires_grad:
                    # remove as '.'-character is not allowed in buffers
                    s_name = name.replace(".", "")
                    self.m_name2s_name.update({name: s_name})
                    self.register_buffer(s_name, p.clone().detach().data)

            self.collected_params = list()

    def reset_num_updates(self):
        del self.num_updates
        self.register_buffer("num_updates", torch.tensor(0, dtype=torch.int))

    def forward(self, model):
        decay = self.decay

        if self.num_updates >= 0:
            self.num_updates += 1
            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))

        with torch.no_grad():
            m_param = dict(model.named_parameters())
            shadow_params = dict(self.named_buffers())

            for key in m_param:
                if m_param[key].requires_grad:
                    sname = self.m_name2s_name[key]
                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                    shadow_params[sname].sub_((1.0 - decay) * (shadow_params[sname] - m_param[key]))
                else:
                    assert key not in self.m_name2s_name

    def copy_to(self, model):
        m_param = dict(model.named_parameters())
        shadow_params = dict(self.named_buffers())
        for key in m_param:
            if m_param[key].requires_grad:
                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
            else:
                assert key not in self.m_name2s_name

    def store(self, parameters):
        """
        Save the current parameters for restoring later.

        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored.
        """

        self.collected_params = [param.clone() for param in parameters]

    def restore(self, parameters):
        """
        Restore the parameters stored with the `store` method.
        Useful to validate the model with EMA parameters without affecting the
        original optimization process. Store the parameters before the `copy_to` method.
        After validation (or model saving), use this to restore the former parameters.

        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters.
        """

        for c_param, param in zip(self.collected_params, parameters):
            param.data.copy_(c_param.data)
